Resolving closely spaced acoustic sources when the noise power is varying in space and time has been studied extensively.
As part of ODAS, we rely on the low-complexity Steered-Response Power Phase Transform (SRP-PHAT) to estimate DOAs [1]}, [2]}.
Many other methods could be used such as adaptive beamformers [3]} or compressive DOA estimation
[4]}, [5]}, [6]}, [7]}.
Sparse Bayesian learning (SBL) [5]}, [9]}, [10]}, [11]}, [12]} could also be used.
Multiple Signal Classification (MUSIC) [13]} is also common for localization in noisy environments [14]}, [15]}.
DOA estimation could also rely on machine leaning [16]}, [17]}.
Since each circular array has well-defined geometry classical array processing seems more appropriate, and machine learning could be used in the combination of the arrays.
More features than just DOA could be beneficial for localization [18]},
e.g., echoes [19]}.
As a low-complexity approach, just three microphones have been used [20]}.
